{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/priorcvae-scalable-mcmc-parameter-inference","title":"PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling","arxiv_id":"2304.04307","date":"2023-04-09","proceeding":null,"authors":["Elizaveta Semenova","Prakhar Verma","Max Cairney-Leeming","Arno Solin","Samir Bhatt","Seth Flaxman"],"abstract":"Recent advances have shown that GP priors, or their finite realisations, can be encoded using deep generative models such as variational autoencoders (VAEs). These learned generators can serve as drop-in replacements for the original priors during MCMC inference. While this approach enables efficient inference, it loses information about the hyperparameters of the original models, and consequently makes inference over hyperparameters impossible and the learned priors indistinct. To overcome this limitation, we condition the VAE on stochastic process hyperparameters. This allows the joint encoding of hyperparameters with GP realizations and their subsequent estimation during inference. Further, we demonstrate that our proposed method, PriorCVAE, is agnostic to the nature of the models which it approximates, and can be used, for instance, to encode solutions of ODEs. It provides a practical tool for approximate inference and shows potential in real-life spatial and spatiotemporal applications.","url_abs":"https://arxiv.org/abs/2304.04307v3","url_pdf":"https://arxiv.org/pdf/2304.04307v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"priorcvae-scalable-mcmc-parameter-inference","repo_url":"https://github.com/elizavetasemenova/priorCVAE_jax","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"priorcvae-scalable-mcmc-parameter-inference","repo_url":"https://github.com/elizavetasemenova/priorvae","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2304.04307","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.04307"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/elizavetasemenova/priorvae","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/elizavetasemenova/priorCVAE_jax","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":8},"by_repo_kind":{"official":{"samples":8,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"2afd74601caf497c","entry":"frobenius_norm_of_diff","repo":"elizavetasemenova/priorCVAE_jax","repo_kind":"official","path":"priorCVAE/diagnostics/gp_metrics.py","file_url":"https://github.com/elizavetasemenova/priorCVAE_jax/blob/HEAD/priorCVAE/diagnostics/gp_metrics.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"2afd74601caf497c"}},{"code_sha256_prefix":"edc317cb9a6c1d03","entry":"kl_divergence","repo":"elizavetasemenova/priorCVAE_jax","repo_kind":"official","path":"priorCVAE/losses/loss_units.py","file_url":"https://github.com/elizavetasemenova/priorCVAE_jax/blob/HEAD/priorCVAE/losses/loss_units.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"edc317cb9a6c1d03"}},{"code_sha256_prefix":"2ee252d484de456e","entry":"load_model_params","repo":"elizavetasemenova/priorCVAE_jax","repo_kind":"official","path":"priorCVAE/utility.py","file_url":"https://github.com/elizavetasemenova/priorCVAE_jax/blob/HEAD/priorCVAE/utility.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"2ee252d484de456e"}},{"code_sha256_prefix":"54604ca0819c07f4","entry":"mean_squared_loss","repo":"elizavetasemenova/priorCVAE_jax","repo_kind":"official","path":"priorCVAE/losses/loss_units.py","file_url":"https://github.com/elizavetasemenova/priorCVAE_jax/blob/HEAD/priorCVAE/losses/loss_units.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"54604ca0819c07f4"}},{"code_sha256_prefix":"465f00799376f1b7","entry":"numpy_collate","repo":"elizavetasemenova/priorCVAE_jax","repo_kind":"official","path":"priorCVAE/utility.py","file_url":"https://github.com/elizavetasemenova/priorCVAE_jax/blob/HEAD/priorCVAE/utility.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"465f00799376f1b7"}},{"code_sha256_prefix":"4cd4e71429c59572","entry":"sample_covariance","repo":"elizavetasemenova/priorCVAE_jax","repo_kind":"official","path":"priorCVAE/diagnostics/gp_metrics.py","file_url":"https://github.com/elizavetasemenova/priorCVAE_jax/blob/HEAD/priorCVAE/diagnostics/gp_metrics.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"4cd4e71429c59572"}},{"code_sha256_prefix":"9cd457c7882800c2","entry":"scaled_sum_squared_loss","repo":"elizavetasemenova/priorCVAE_jax","repo_kind":"official","path":"priorCVAE/losses/loss_units.py","file_url":"https://github.com/elizavetasemenova/priorCVAE_jax/blob/HEAD/priorCVAE/losses/loss_units.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"9cd457c7882800c2"}},{"code_sha256_prefix":"45e02013457c2d1a","entry":"sq_euclidean_dist","repo":"elizavetasemenova/priorCVAE_jax","repo_kind":"official","path":"priorCVAE/utility.py","file_url":"https://github.com/elizavetasemenova/priorCVAE_jax/blob/HEAD/priorCVAE/utility.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"45e02013457c2d1a"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}